qdrant-vector-search Skill
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance. Published by NousResearch in hermes-agent.
What is qdrant-vector-search Skill?
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance. Published by NousResearch in hermes-agent. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Database workflows
- Deployment
- Data analysis
- Research
- Database workflows use cases
Technical details
- Install or run with Copy skill directory
When to use qdrant-vector-search Skill
- Use it for database workflows.
- Use it for deployment.
- Use it for data analysis.
- Use it for research.
Built with
Editorial notes
Source
- Creator: NousResearch
- Repository: NousResearch/hermes-agent
- Skill file: optional-skills/mlops/qdrant/SKILL.md
What it does
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Skill instructions
Qdrant - Vector Similarity Search Engine High-performance vector database written in Rust for production RAG and semantic search. When to use Qdrant Use Qdrant when: - Building production RAG systems requiring low latency - Need hybrid search (vectors + metadata filtering) - Require horizontal scaling with sharding/replication - Want on-premise deployment with full data control - Need multi-vector storage per record (dense + sparse) - Building real-time recommendation systems Key features: - Rust-powered: Memory-safe, high performance - Rich filtering: Filter by any payload field during search - Multiple vectors: Dense, sparse, multi-dense per point - Quantization: Scalar, product, binary for memory efficiency - Distributed: Raft consensus, sharding, replication - REST + gRPC: Both APIs with full feature parity Use alternatives instead: - Chroma: Simpler setup, embedded use cases - FAISS: Maximum raw speed, research/batch processing - Pinecone: Fully managed, zero ops preferred - Weavi
Explore related resources
Frequently asked questions
What is qdrant-vector-search?
qdrant-vector-search is a open-source AI agent skill with Copy skill directory. High-performance vector similarity search engine for RAG and semantic search.
Who is qdrant-vector-search best for?
qdrant-vector-search is best for reusing agent instructions, scripts, and references, database workflows, deployment workflows, data analysis workflows.
How do I install qdrant-vector-search?
Install or run qdrant-vector-search using Copy skill directory. Check qdrant-vector-search for the latest setup command.
Is qdrant-vector-search actively maintained?
qdrant-vector-search may need a closer maintenance check before production use.
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